CN110088573B

CN110088573B - Apparatus for determining vehicle position and associated method

Info

Publication number: CN110088573B
Application number: CN201780078146.XA
Authority: CN
Inventors: 奥利弗·杜斯; 安德里·马蒂尼瓦
Original assignee: Here Global BV
Current assignee: Here Global BV
Priority date: 2016-12-20
Filing date: 2017-12-20
Publication date: 2023-06-23
Anticipated expiration: 2037-12-20
Also published as: US20200088526A1; EP3339807A1; US11493346B2; CN110088573A; WO2018115142A1; EP3339807B1

Abstract

An apparatus comprising a processor and a memory containing computer program code, the memory and computer program code configured to, with the processor, enable the apparatus to at least: a current location of a vehicle on a road network including a plurality of different travel paths is determined for each of the different travel paths using a respective recursive bayesian filter, wherein each recursive bayesian filter is configured to compare data received from sensors on the vehicle with predetermined map data for the respective travel path to calculate a probability that the vehicle is currently located on the respective travel path and a probability distribution of possible locations along the travel path.

Description

Apparatus for determining vehicle position and associated method

Technical Field

The present disclosure relates to the field of route navigation, associated methods and apparatus, and in particular to apparatus configured to determine a current location of a vehicle on a road network comprising a plurality of different travel paths using respective recursive Bayesian (Bayesian) filters for each of the different travel paths. Certain disclosed example aspects/embodiments relate to portable or car navigation devices. In-vehicle navigation devices may include hardware having similar characteristics as mobile navigation devices, but typically include less power hardware designed for tasks. In the present case, the portable or car navigation device should be able to access a database (online or offline) containing map data and sensor data from one or more sensors (e.g., GNSS, inertial measurement unit, accelerometer, compass, magnetometer, gyroscope, barometer/altitude sensor, camera, liDAR, RADAR, ultrasonic sensor, etc.).

Background

Research is currently underway to develop improved navigation devices that can provide users with more detailed and accurate information about road networks to further assist route navigation.

One or more aspects/embodiments of the present disclosure may or may not address this issue.

Listing or discussion of a prior-published document or any background in this specification should not be taken as an acknowledgement that the document or background is part of the state of the art or is common general knowledge.

Disclosure of Invention

According to a first aspect, there is provided an apparatus comprising a processor and a memory including computer program code, the memory and computer program code configured to, with the processor, enable the apparatus to at least:

a respective recursive bayesian filter is used for each of the different travel paths to determine a current position of the vehicle on a road network comprising a plurality of different travel paths,

wherein each recursive bayesian filter is configured to compare data received from sensors on the vehicle with predetermined map data for the respective travel path to calculate a probability that the vehicle is currently located on the respective travel path and a probability distribution of possible locations along that travel path.

The apparatus may be configured to determine the plurality of different travel paths.

The apparatus may be configured to map at least one known location of the vehicle with the predetermined map data to determine the plurality of different travel paths.

The apparatus may be configured to extend a travel path based on the predetermined map data when it is determined that the current position of the vehicle is proximal to an end of the travel path.

The apparatus may be configured to determine one or more additional travel paths if the likelihood associated with each of the different travel paths indicates that these travel paths are not sufficiently suitable for the sensor data.

The apparatus may be configured to determine the current location of the vehicle at least in part by excluding any travel paths for which an associated likelihood is below a predefined threshold.

The apparatus may be configured to determine the current location of the vehicle at least in part by excluding any travel paths whose ratio of associated likelihood to likelihood of a most likely travel path is below a predefined threshold.

The apparatus may be configured to determine the current location of the vehicle at least in part by excluding all travel paths outside of a predefined number of travel paths having a highest associated likelihood while preserving any travel paths having an associated likelihood equal to a lowest associated likelihood of remaining travel paths.

The apparatus may be configured to determine the current location of the vehicle as one or more of the possible locations along the travel path having the greatest associated likelihood (e.g., a distributed "expected value").

Each recursive bayesian filter may be a kalman filter configured to generate a gaussian distribution of possible locations along the respective travel path, and wherein the apparatus may be configured to determine the current location of the vehicle as a center of the gaussian distribution associated with the travel path having the greatest associated likelihood.

The apparatus may be configured to normalize the likelihood calculated by the recursive bayesian filter to obtain a probability distribution of the possible travel paths.

Each recursive bayesian filter may be configured to compare the sensor with predetermined map data to include one or more additional physical state variables in the probability distribution.

The one or more additional physical state variables may include at least one of speed, heading, angular speed, odometer scale factor, and gyroscope calibration parameters.

The travel path is unidirectional, and wherein the apparatus may be configured to add the same travel path extending in opposite directions if the heading of the vehicle deviates more than 90 ° from the direction of a particular travel path.

Each recursive bayesian filter may be configured to compare real-time sensor data with the predetermined map data.

The apparatus may be configured to store sensor data after the sensor data has been received, and each recursive bayesian filter may be configured to compare the stored sensor data with the predetermined map data.

The sensors on the vehicle may be one or more of global or regional navigation satellite system receivers (e.g., for NAVSTAR GPS, GLONASS, beidou, galileo, GAGAN, or IRNSS), inertial sensors, cameras, accelerometers, and gyroscopes.

The plurality of different travel paths may be two-dimensional travel paths or three-dimensional travel paths.

The device may be one or more of the following: electronic devices, portable communication devices, mobile phones, personal digital assistants, tablet computers, tablet phones, desktop computers, notebook computers, servers, smartphones, smartwatches, smart glasses, portable satellite navigation devices, in-vehicle satellite navigation devices, and modules for one or more of them.

According to another aspect, there is provided a method comprising:

The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated or understood by the skilled artisan.

Corresponding computer programs (which may or may not be recorded on a carrier) for implementing one or more of the methods disclosed herein are also within the present disclosure and encompassed by one or more of the described example embodiments.

The disclosure includes one or more corresponding aspects, example embodiments, or features, alone or in various combinations, whether or not specifically stated (including what is required) in the combinations or alone. Respective means for performing one or more of the discussed functions are within the present disclosure.

The above summary is intended to be merely exemplary and non-limiting.

Drawings

The description is now given, by way of example only, with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an apparatus configured to perform one or more methods described herein;

fig. 2a schematically shows an example of a road network;

fig. 2b schematically shows three different travel paths on the road network of fig. 2 a;

fig. 3a schematically shows a probability distribution of possible positions along a particular travel path;

fig. 3b schematically shows a probability distribution of possible positions further along the specific travel path of fig. 3 a;

fig. 4a schematically shows a probability distribution of possible positions on a travel path without drivable deviation;

fig. 4b schematically shows a probability distribution of possible locations at a road intersection;

FIG. 5 schematically illustrates the main steps of the method described herein; and is also provided with

Fig. 6 illustrates a computer-readable medium comprising a computer program configured to perform, control, or implement one or more methods described herein.

Detailed Description

The navigation device needs to be able to determine the current position of the vehicle relative to the map in order to provide route guidance for the driver of the vehicle. This is typically accomplished using one or more on-board sensors, such as Global Navigation Satellite Systems (GNSS), inertial sensors, cameras, accelerometers, and gyroscopes. The latest solutions to this problem include a number of individual steps. First, dead reckoning, which typically combines known reference positions (e.g., from GNSS) with sensor data (e.g., from inertial sensors), is used to determine a trajectory and infer this trajectory when no satellite signals are available (e.g., in a tunnel). Map matching is then performed to correlate the determined trajectory with the most likely travel path on the road network. Since there is often a significant change between the determined trajectory and the most likely travel path (e.g., due to atmospheric conditions or reflections affecting GNSS signals, or drift in dead reckoning), the map-matched position is then fed back to the dead reckoning unit so that it can be used to correct for errors in the trajectory. Finally, if a lane assist system is available, it can be used to track on which lane the vehicle is currently. This module takes as input the number of lanes available on the current road and tracks the lane crossing, which allows tracking the lateral position of the vehicle on the road.

One problem with this approach is that some areas of the road network include adjacent roads, which are indistinguishable from each other based on data from certain on-board sensors. For example, there are situations where a GNSS signal indicates that a vehicle is traveling along an expressway, but in fact it is on a single lane parallel to a particular section of the expressway (or vice versa). In this case, the road guidance provided by the navigation device does not match the current travel path, which is useless to the driver and may create a potential hazard to the driver and other road users (especially considering that different classes of roads typically have different speed limits).

An apparatus and associated method that can address this issue will now be described.

Fig. 1 illustrates a device 101 configured to perform one or more methods described herein. The device 101 may be at least one of the following: electronic devices, portable communication devices, mobile phones, personal digital assistants, tablet computers, tablet phones, desktop computers, notebook computers, servers, smartphones, smartwatches, smart glasses, portable satellite navigation devices, in-vehicle satellite navigation devices, and modules for one or more of them.

In this example, the apparatus 101 includes a processor 102, a memory 103, a transceiver 104, a power source 105, an electronic display 106, and a microphone 107, which are electrically connected to each other by a data bus 108. The processor 102 is configured for general operation of the device 101 by providing signaling to and receiving signaling from other components to manage their operation. The memory 103 is configured to store computer program code configured to perform, control or implement the operations of the device 101. The memory 103 may also be configured to store settings of other components. The processor 102 may access the memory 103 to retrieve component settings in order to manage the operation of other components. The processor 102 may be a microprocessor, including an Application Specific Integrated Circuit (ASIC). Memory 103 may be a temporary storage medium such as a volatile random access memory. Alternatively, memory 103 may be a permanent storage medium, such as a hard disk drive, flash memory, or nonvolatile random access memory.

Transceiver 104 is configured to transmit data to and/or receive data from other devices/apparatuses while power supply 105 is configured to provide power to other components to achieve its functionality, and may include one or more of a power adapter, a battery, a capacitor, a supercapacitor, and a hybrid battery capacitor. Electronic display 106 may be an LED, LCD, or plasma display and is configured to display visual content stored on device 101 (e.g., on a storage medium) or received by device 101 (e.g., via a transceiver). Similarly, microphone 107 is configured to output audio content stored on device 101 or received by device 101. The visual and audio content may include related components of the combined audio visual content. In some examples, the audio and/or visual content may include navigational content (e.g., geographic information and direction of travel, distance, speed, or time).

The methods described herein depart from the above-mentioned methods of fusing multiple sensor inputs to derive a location for subsequent map matching. Instead, the assumption that the vehicle is traveling on the road network is utilized to derive a plurality of different travel paths (or "road tracks") that represent the likelihood that the vehicle must traverse the road network. In this context, the travel path consists of a series of drivable, successive road segments which reflect the trajectory of the vehicle up to now in two or three dimensions. The inventive device may be configured to determine a plurality of different travel paths by map matching at least one known location of the vehicle with predetermined map data. This involves finding a list of road segments in the vicinity of a known location and, for each road segment, creating a travel path that includes the road segment. If the road segment can travel in two directions, two travel paths may be created that extend in opposite directions from each end of the road segment.

Fig. 2a and 2b show a map of the road network indicating an initial position 209 (not necessarily the current position) of the vehicle and three possible travel paths r1-r3 through the road network as determined by the device. As can be seen, the three different travel paths r1-r3 intersect at a common point 210 on a road network, which may be a circular road or an intersection or the like.

After a plurality of different travel paths r1-r3 have been determined, the device is configured to compare data received from the sensor (or sensors) on the vehicle with predetermined map data (e.g., digital maps, street and aerial images, road coordinates, speed limits, road limits, etc.) for each travel path r1-r3 using a respective recursive bayesian filter. For example, the sensor may be a global navigation satellite system receiver and the device may be configured to compare geographic coordinates from satellite data with coordinates of a stored digital map. Similarly, the sensor may be a camera and the device may be configured to compare images recorded by the camera with stored street images taken at various locations on the road network. The latter case may be particularly suitable for detecting lanes or exits on a section of road.

Each recursive bayesian filter calculates the likelihood that the vehicle is currently located on the respective travel path r1-r3, and the probability distribution of possible locations along this travel path r1-r3. Thus, the likelihood is a measure of how well the respective travel paths r1-r3 fit the sensor data. However, in some cases, the likelihood associated with each of the different travel paths r1-r3 may indicate that none of these travel paths r1-r3 are sufficiently suitable for sensor data (e.g., because the likelihood is below a predefined threshold). In this case, the apparatus may be configured to determine one or more additional travel paths. This may be achieved by map matching (as described above) or by considering other travel paths that are close to the existing travel path. In some cases, no additional travel path may fit the sensor data, which may indicate that there is an error in the map data. This error can be resolved by obtaining the latest map data of the road intersection.

While recursive bayesian filters will typically be configured to compare real-time sensor data to predetermined map data to enable determination of the current location of the vehicle, they may use sensor data from a previous portion of the vehicle's trajectory (e.g., which has been stored by the device) to predict the current location. This enables the present method to be used for recursive bayesian smoothing, in addition to or instead of recursive bayesian filtering.

This process is iteratively repeated to ensure that the current location of the vehicle is up-to-date (e.g., in a predefined time step or whenever new sensor data becomes available). The current location of the vehicle may then be determined by considering the likelihood and probability distribution of each recursive bayesian filter. For example, the apparatus may be configured to determine the current location of the vehicle as one or more of the possible locations along the travel path having the greatest associated likelihood.

Fig. 3a and 3b show the

probability distributions

311, 312 calculated at two different points on the travel path 313 having the greatest associated likelihood. In this example, each recursive bayesian filter is a kalman filter (but it may be another type of filter, such as an unscented kalman filter) configured to produce a gaussian distribution of possible locations along the respective travel path 313, and the apparatus is configured to determine the current location of the vehicle as the center of the gaussian distribution (as indicated by the star 314). It should be noted that when the determined vehicle position approaches the end of the current road segment 315, the filter may require additional information about the travel path 313 in order to function. In this case, the apparatus is configured to extend the travel path 313 based on predetermined map data when it is determined that the current position of the vehicle is proximal to the end of the travel path 313 (i.e., obtain map data of the next road segment 316 when the vehicle approaches the end of the current road segment 315).

Fig. 4a and 4b

show probability distributions

417, 418 calculated at two different points on another travel path 419 (i.e. a separate vehicle track to one travel path shown in fig. 3a and 3 b) with the greatest associated likelihood. In fig. 4a, the travel path 419 is relatively straight and has no drivable deviation (as in fig. 3a and 3 b). However, as the vehicle approaches the end of the current road section 420, the road diverges in different directions, creating two different

possible travel paths

421, 422. Thus, the device considers the likelihood of each

travel path

421, 422 separately using a respective bayesian filter in order to track vehicle position.

As the vehicle continues to travel along the driver's selected trajectory, more sensor data is obtained and one travel path 421 accumulates more likelihood than the other travel path 422. Thus, it is believed that the driver does not follow the unlikely path 422. As a result, unlikely travel paths 422 and their associated filters may be discarded from analysis. In this way, when less likely paths 422 are excluded, an increase in the number of paths at the intersection is compensated. This allows the amount of data to be processed to be at a manageable level. Indeed, the device may be configured to exclude any travel path for which the associated likelihood is below a predefined threshold; excluding any travel paths whose ratio of the associated likelihood to the likelihood of the most likely travel path is below a predefined threshold; or excluding all travel paths outside of the predefined number of travel paths having the highest associated likelihood while preserving any travel paths having an associated likelihood equal to the lowest associated likelihood of the remaining travel paths.

In some examples, the apparatus may be configured to determine a probability distribution over all possible travel paths instead of a plurality of respective likelihoods. This can be achieved by normalizing the likelihood.If different travel paths have the possibility l ₁ ，l ₂ ，…，l _n The probability distribution is composed of p _i The individual probabilities for each path i are defined, wherein:

in addition to location, each recursive bayesian filter may be configured to compare the sensor to predetermined map data to include one or more additional physical state variables in the probability distribution depending on which sensors and map data are available. For example, the additional physical state variables may include at least one of speed, heading, angular speed, odometer scale factor, and gyroscope calibration parameters. In this way, the inventive device can determine not only the current position of the vehicle, but also its speed and direction, etc. When the recursive bayesian filter takes into account a plurality of physical state variables, it outputs a joint distribution of all the variables, but this can then be marginalized to obtain a probability distribution for one particular variable, if desired. For example, if a state contains three physical variables x, y, and z, the edge distribution for x can be obtained using the following equation:

p (x) = ≡≡p (x, y, z) dydz equation 2

Additional variables may also be used in the bayesian filter to determine the most likely travel path (e.g., because different travel paths may have different associated shapes and speed limits). One such example is when the heading of the vehicle deviates more than 90 ° from the direction of the particular travel path. In this case, the apparatus may be configured to add the same travel path extending in opposite directions.

Fig. 5 shows the main steps 523 to 525 of the method described herein. The method generally includes: using a respective recursive bayesian filter for each of a plurality of different travel paths on the road network to compare data received from sensors on the vehicle with predetermined map data 523 for the respective travel paths; calculating a probability distribution 524 of the likelihood that the vehicle is currently located on the corresponding travel path and the likely location along the travel path; and determining a current location 525 of the vehicle on the road network.

Fig. 6 illustrates a computer/processor readable medium 626 that provides a computer program according to one embodiment. The computer program may comprise computer code configured to perform, control or implement one or more of the method steps 523 to 525 of fig. 5. The computer/processor readable medium 626 in this example is an optical disc, such as a Digital Versatile Disc (DVD) or a Compact Disc (CD). In other embodiments, the computer/processor readable medium 626 can be any medium that has been programmed in a manner to perform the functions of the present invention. The computer/processor readable medium 626 may be a fixed or removable memory device such as a hard drive, a solid state drive, a memory stick (e.g., a USB memory stick), or a memory card (e.g., compact flash, SD, mini SD, micro SD, or nano SD). The computer code may be installed at the factory where the apparatus (e.g., a portable or in-vehicle satellite navigation device) or a vehicle including the apparatus is assembled. The system may be updated later via the computer/processor readable medium 626 described above.

Other embodiments depicted in the drawings have been provided with reference numerals corresponding to similar features of the previously described embodiments. For example, feature number 1 may also correspond to the numbers 101, 201, 301, etc. These numbered features may appear in the figures, but may not be directly referenced in the description of these particular embodiments. These are still provided in the drawings to aid in understanding other embodiments, particularly with respect to features of similar previously described embodiments.

Those skilled in the art will appreciate that any of the mentioned devices/means and/or other features of the specifically mentioned devices/means may be provided by a device arranged such that they are configured to perform the desired operation only when enabled, e.g. switched on or the like. In such cases, they may not necessarily have the appropriate software loaded into active memory in an inactive (e.g., off state) and only the appropriate software loaded in an active (e.g., on state). The device may include hardware circuitry and/or firmware. The device may include software loaded onto the memory. Such software/computer programs may be recorded on the same memory/processor/functional unit and/or on one or more memory/processor/functional units.

In some embodiments, the particular mentioned device/apparatus may be preprogrammed with the appropriate software to perform the desired operation, and wherein the appropriate software may be enabled for use by a user to download a "key", for example to unlock/enable the software and its associated functionality. Advantages associated with such embodiments may include reduced requirements for downloading data when further functionality is required for the device, and this may be useful in instances where the sensing device has sufficient capacity to store such preprogrammed software to obtain functionality that a user may not be able to enable.

It will be appreciated that any of the mentioned device circuits/elements/processors may have other functions in addition to the mentioned functions, and that these functions may be performed by the same device/circuit/element/processor. One or more of the disclosed aspects may encompass an electronic distribution of an associated computer program and a computer program (which may be source/transport encoded) recorded on a suitable carrier (e.g., memory, signal).

It will be appreciated that any "computer" described herein may include a collection of one or more individual processors/processing elements that may or may not be located on the same circuit board or the same area/location of a circuit board or even on the same device. In some embodiments, one or more of any of the mentioned processors may be distributed across multiple devices. The same or different processors/processing elements may perform one or more functions described herein.

It should be appreciated that the term "signaling" may refer to one or more signals transmitted as a series of transmitted and/or received signals. A series of signals may comprise one, two, three, four or even more individual signal components or different signals used to compose the signaling. Some or all of these individual signals may be transmitted/received simultaneously in sequence and/or so that they overlap each other in time.

With reference to any discussion of any reference to a computer and/or processor and memory (e.g., including ROM, CD-ROM, etc.), these may include a computer processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), and/or other hardware components that have been programmed in a manner to perform the functions of the invention.

The applicant hereby discloses in isolation each individual feature described herein and any combination of two or more such features, to the extent that such features or combinations are capable of being carried out based on the present specification as a whole in the light of the common general knowledge of a person skilled in the art, irrespective of whether such features or combinations of features solve any problems disclosed herein; and do not limit the scope of the claims. The applicant indicates that the disclosed aspects/embodiments may consist of any such individual feature or combination of features. It will be apparent to those skilled in the art in view of the foregoing description that various modifications may be made within the scope of the disclosure.

While there have been shown and described and pointed out fundamental novel features of the invention as applied to different embodiments thereof, it will be understood that various omissions and substitutions and changes in the form and details of the devices and methods described may be made by those skilled in the art without departing from the spirit of the invention. For example, it is expressly intended that all combinations of those elements and/or method steps which perform substantially the same function in substantially the same way to achieve the same results are within the scope of the invention. In addition, it should be appreciated that, structures and/or elements and/or method steps shown and/or described in connection with any disclosed form or embodiment may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. Furthermore, in the claims means-plus-function clauses (means-plus-function clase) are intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures.

Claims

1. An apparatus for navigation comprising a processor and a memory containing computer program code, the memory and computer program code configured to, with the processor, enable the apparatus to at least:

determining a plurality of different travel paths of the vehicle on the road network by map-matching at least one known location of the vehicle with predetermined map data of the road network, each of the plurality of different travel paths comprising a drivable series of consecutive road segments reflecting in two or three dimensions the trajectory of the vehicle so far,

determining a current location of the vehicle on the road network using a respective recursive bayesian filter for each of the different travel paths, each recursive bayesian filter being configured to compare data received from sensors on the vehicle with predetermined map data for the respective travel path to calculate a probability that the vehicle is currently located on the respective travel path and a probability distribution of possible locations along the travel path; and

one or more additional travel paths are determined in the event that the likelihood associated with each of the different travel paths is below a predefined threshold.

2. The apparatus of claim 1, wherein the apparatus is configured to extend the travel path based on the predetermined map data when it is determined that the current position of the vehicle is proximal to an end of travel path.

3. The apparatus of claim 1 or 2, wherein the apparatus is configured to determine the current location of the vehicle at least in part by:

excluding any travel paths for which the associated likelihood is below a predefined threshold;

excluding any travel paths whose ratio of the associated likelihood to the likelihood of the most likely travel path is below a predefined threshold; or (b)

All travel paths outside the predefined number of travel paths having the highest associated likelihood are excluded while any travel paths having an associated likelihood equal to the lowest associated likelihood of the remaining travel paths are retained.

4. The apparatus of claim 1 or 2, wherein the apparatus is configured to determine the current location of the vehicle as one or more of the possible locations along the travel path having the greatest associated likelihood.

5. The apparatus according to claim 4, wherein each recursive bayesian filter is a kalman filter configured to produce a gaussian distribution of possible locations along the respective travel path, and wherein the apparatus is configured to determine the current location of the vehicle as a center of the gaussian distribution associated with the travel path having the greatest associated likelihood.

6. The apparatus according to claim 1 or 2, wherein the apparatus is configured to normalize the likelihood calculated by the recursive bayesian filter to obtain a probability distribution of possible travel paths.

7. The apparatus of claim 1 or 2, wherein each recursive bayesian filter is configured to compare the sensor with predetermined map data to include one or more additional physical state variables in the probability distribution.

8. The apparatus of claim 7, wherein the one or more additional physical state variables comprise at least one of speed, heading, angular speed, odometer scale factor, and gyroscope calibration parameters.

9. The apparatus of claim 8, wherein the travel paths are unidirectional, and wherein the apparatus is configured to add identical travel paths extending in opposite directions if the heading of the vehicle deviates more than 90 ° from a travel direction associated with one of the travel paths.

10. The apparatus of claim 1 or 2, wherein each recursive bayesian filter is configured to compare real-time sensor data with the predetermined map data.

11. The apparatus of claim 1 or 2, wherein the apparatus is configured to store sensor data after it has been received, and each recursive bayesian filter is configured to compare the stored sensor data with the predetermined map data.

12. The apparatus of claim 1 or 2, wherein the plurality of different travel paths comprises two or more travel paths intersecting at a common point on the road network.

13. A computer-implemented method for navigation, comprising:

14. A computer readable medium having stored thereon a computer program comprising computer code configured to perform the method of claim 13.